Relevance workbench
In this workbench, you can compare our Elastic Learned Sparse Encoder model (with or without RRF) and traditional textual search using BM25.
Start comparing different hybrid search techniques using TMDB's movies dataset as sample data. Or fork the code and ingest your own data to try it on your own!
Try these queries to get started:
- "The matrix"
- "Movies in Space"
- "Superhero animated movies"
Notice how some queries work great for both search techniques. For example, 'The Matrix' performs well with both models. However, for queries like "Superhero animated movies", the Elastic Learned Sparse Encoder model outperforms BM25. This can be attributed to the semantic search capabilities of the model.
Explore similar demos
Observability
Monitoring your infrastructure with OpenTelemetry
Observability 101: Lesson 3 of 4 — In this 15 minute lab, use Elastic Observability to dive into a java issue with high-cpu usage using the data collected from the Elastic Distributions of OpenTelemetry (EDOT). Resolve this issue using the Elastic AI Assistant as well as leverage runbook to prevent recurrence.
Sécurité
Aperçu de Security
Elastic Security équipe les analystes pour prévenir, détecter et répondre aux menaces. La solution propose des fonctionnalités de SIEM, Endpoint Security, recherche de menaces, monitoring du cloud et bien plus encore.
Observability
Root cause analysis with AI
Observability 101: Lesson 4 of 4 — Explore this 15 minute lab and use Elastic Observability's machine learning and AI technologies to perform Root Cause Analysis (RCA) of a customer-reported issue. Combine clues from traces and logs at scale with Elastic's AI Assistant to come to a faster resolution.